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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-775a75ab89","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-4d8b3829ba",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-e8e5222991","paragraphs":["&lt;!-- ------------------------ --&gt;\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn this Quickstart, we're going to focus on data engineering with Snowflake. We'll specifically build an end-to-end data pipeline with Snowflake. We'll apply the  \u003Cstrong\u003EIngestion-Transformation&ndash;Delivery\u003C/strong\u003E framework, also known as \u003Cstrong\u003EI-T-D\u003C/strong\u003E, to build the pipeline.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIngestion\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWe'll load data from:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake Marketplace\u003C/li\u003E\u003Cli\u003EAWS S3\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETransformation\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWe'll transform our data using:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESQL\u003C/li\u003E\u003Cli\u003EViews\u003C/li\u003E\u003Cli\u003EUser-defined functions (UDFs)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDelivery\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWe'll deliver a final data product using:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EStreamlit in Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESome basic familiarity with SQL\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You&rsquo;ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake for data engineering\u003C/li\u003E\u003Cli\u003EThe Ingestion-Transformation-Delivery framework, or I-T-D, for data pipelines\u003C/li\u003E\u003Cli\u003EData sharing from Snowflake Marketplace\u003C/li\u003E\u003Cli\u003EHow to load data from AWS S3 blob storage\u003C/li\u003E\u003Cli\u003EHow to perform transformations against data using SQL, Python, Views, UDFs, Stored Procedures\u003C/li\u003E\u003Cli\u003EHow to deliver an end data product using Streamlit in Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You&rsquo;ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA free Snowflake trial account: \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&amp;utm_medium=developer-guides&amp;trial=student&amp;cloud=aws&amp;region=us-west-2&amp;utm_campaign=introtosnowflake&amp;utm_cta=developer-guides\"\u003Ehttps://signup.snowflake.com/\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EThe companion GitHub repo that contains the code to complete this lab: \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering\"\u003Esfguide-snowflake-northstar-data-engineering\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You&rsquo;ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn end-to-end data pipeline in Snowflake\u003C/li\u003E\u003C/ul\u003E\n&lt;!-- ------------------------ --&gt;\n","\u003Ch2\u003EOpen a Snowflake Trial Account\u003C/h2\u003E\n","\u003Cp\u003ETo complete this lab, you'll need a Snowflake account. A free Snowflake trial account will work just fine. To open one:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&amp;utm_medium=developer-guides&amp;trial=student&amp;cloud=aws&amp;region=us-west-2&amp;utm_campaign=introtosnowflake&amp;utm_cta=developer-guides\"\u003Ehttps://signup.snowflake.com/\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EStart the account creation by completing the first page of the form on the page.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the next section of the form,  be sure to set the Snowflake edition to &quot;Enterprise (Most popular&quot;).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect &quot;AWS &ndash;&nbsp;Amazon Web Services&quot; as the cloud provider.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect &quot;US West (Oregon)&quot; as the region.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EComplete the rest of the form and click &quot;Get started&quot;.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-northstar-data-engineering/trial.png?v=69367252\" alt=\"trial\"\u003E\u003C/p\u003E\n&lt;!-- ------------------------ --&gt;\n","\u003Ch2\u003EUnderstand The Pipeline We'll Build\u003C/h2\u003E\n","\u003Cp\u003ETasty Bytes is a food truck company that operates globally in many countries. You're a data engineer on the Tasty Bytes team, and you've recently learned from data analysts on the team that:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESales in the city of Hamburg, Germany dropped to $0 for a few days in the month of February\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAs a data engineer, your goal is to figure out why this happened, and to also build and end-to-end data pipeline that can  keep analysts up-to-date on the weather in Hamburg.\u003C/p\u003E\n","\u003Cp\u003EHere's how we'll do this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIngestion\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ELoad live weather data from Snowflake Marketplace\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELoad Tasty Bytes sales data from an AWS S3 bucket\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETransformation\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EUse SQL to perform transformations\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate views that we can use for data analytics\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate user-defined functions (UDFs) that perform weather-related calculations to enrich the views with more data\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDeliver\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDeliver final insights and a live graph of weather in Hamburg, Germany using Streamlit in Snowflake (write the application in Python)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ELet's get started!\u003C/p\u003E\n&lt;!-- ------------------------ --&gt;\n","\u003Ch2\u003EWeather Data From Snowflake Marketplace\u003C/h2\u003E\n","\u003Cp\u003ELet's start by &quot;loading&quot; the raw weather data into Snowflake. It turns out that &quot;loading&quot; is really the wrong word here.\u003C/p\u003E\n","\u003Cp\u003EWe're using Snowflake's unique data sharing capability in Snowflake Marketplace. Because of this, we don't actually need to copy any data to our Snowflake account with any logic. Instead, we can directly access the weather data shared by a trusted provide in Snowflake Marketplace. Let's go ahead and do this.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ELog into your Snowflake account.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on &quot;Marketplace&quot;.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on &quot;Snowflake Marketplace&quot;.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the search bar, search for &quot;pelmorex frostbyte&quot;.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe first result should be &quot;Pelmorex Weather Source: Frostbyte&quot; by the &quot;Pelmorex Weather Source&quot; provider. Click on the listing.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the right, click &quot;Get&quot;.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the ensuing modal, click &quot;Get&quot; once more. Do \u003Cstrong\u003ENOT\u003C/strong\u003E rename the dataset.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis is a live dataset! No need to write ingestion logic to bring the data into your account. The data is maintained and kept fresh by the provider.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-northstar-data-engineering/weathersource.png?v=69367252\" alt=\"data\"\u003E\u003C/p\u003E\n&lt;!-- ------------------------ --&gt;\n","\u003Ch2\u003ELoad Sales Data From AWS S3\u003C/h2\u003E\n","\u003Cp\u003ELet's now load the Tasty Bytes sales data. This data is currently sitting across many CSV files in an AWS S3 bucket. Let's use Snowflake's COPY INTO command to load the data into your Snowflake account.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EStart by creating a new SQL worksheet.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESet your context. Set your role to \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E, and set your compute resource (virtual warehouse) to \u003Cstrong\u003ECOMPUTE_WH\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENext, create the database and schema to store the data that we'll load. Type the following into the worksheet:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DATABASE tasty_bytes;\nCREATE OR REPLACE SCHEMA raw_pos;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\n","\u003Cp\u003ERun this code by highlighting these two statements and clicking the Run button at the top right of the SQL worksheet.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENext, navigate to the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/00_ingestion/copy_into.sql\"\u003E\u003Cstrong\u003Ecopy_into.sql\u003C/strong\u003E\u003C/a\u003E file in the \u003Cstrong\u003E00_ingestion\u003C/strong\u003E folder of the \u003Cstrong\u003Esfguide-snowflake-northstar-data-engineering\u003C/strong\u003E repo. Copy the contents of this file.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate back to the SQL worksheet you opened. Paste in the code you copied under the two lines of SQL you wrote in the previous step.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EStart by creating the file format necessary to load the data into Snowflake. Type the following underneath the &quot;Create a CSV file format here:&quot; comment:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FILE FORMAT tasty_bytes.public.csv_ff\ntype = 'csv';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun this command.\u003C/p\u003E\n\u003Col start=\"8\"\u003E\u003Cli\u003EIn the next block of SQL, with the comment \u003Ccode\u003E-- Specify the file format below:\u003C/code\u003E, we create the Snowflake stage that points to the S3 bucket containing the CSV files. It's currently missing the file format argument that's necessary during the loading process. Below the \u003Ccode\u003Eurl\u003C/code\u003E parameter, type:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Efile_format = tasty_bytes.public.csv_ff;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun this entire command.\u003C/p\u003E\n\u003Col start=\"9\"\u003E\u003Cli\u003E\n","\u003Cp\u003EThe next block of SQL creates a table called \u003Cstrong\u003ECOUNTRY\u003C/strong\u003E in the \u003Cstrong\u003Etasty_bytes.raw_pos\u003C/strong\u003E schema. Run this block of SQL to create the table. Confirm the creation of the table using the object picker on the left-hand side.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's bring it all together now! After creating the table, let's now load the data into this table using the powerful \u003Cstrong\u003ECOPY INTO\u003C/strong\u003E command. At the very bottom of the worksheet, underneath the &quot;Use the COPY INTO command...&quot; comment, type:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECOPY INTO tasty_bytes.raw_pos.country\nFROM @tasty_bytes.public.s3load/raw_pos/country/;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun this command. You should see a success message in the console. Great job!\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWe're not quite done.\u003C/strong\u003E We loaded about 30 rows of data into the \u003Cstrong\u003ECOUNTRY\u003C/strong\u003E table, but we actually need to load close to 1 GB of sales data. We won't do this line-by-line. Instead, you'll use some SQL that we've written for you in advance.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/00_ingestion/load_tasty_bytes.sql\"\u003E\u003Cstrong\u003Eload_tasty_bytes.sql\u003C/strong\u003E\u003C/a\u003E file in the \u003Cstrong\u003E00_ingestion\u003C/strong\u003E folder within the \u003Cstrong\u003Esfguide-snowflake-northstar-data-engineering\u003C/strong\u003E companion repo. Copy the contents.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate back to Snowflake and create a new SQL worksheet. Paste the contents you copied into the worksheet.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERun the entire file at once by clicking the drop-down at the top right, and clicking &quot;Run All&quot;.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EObservations:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EThis will load close to 1 GB of Tasty Bytes sales data from AWS S3 into your Snowflake account.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe script programmatically creates an XL compute resource (virtual warehouse) used for the loading of the data. Once the loading is done, the compute resource is dropped.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThis worksheet contains about 300 lines of SQL. The powerful thing is that you can understand 99% of it because of the SQL you wrote earlier to load data into the \u003Cstrong\u003ECOUNTRY\u003C/strong\u003E table.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAfter running the file, you should have all of the data loaded into your account! Confirm the creation of all the tables (and their data) using the object picker to the left.\u003C/p\u003E\n","\u003Cp\u003EThis completes the \u003Cstrong\u003EIngestion\u003C/strong\u003E aspect of our pipeline for this lab.\u003C/p\u003E\n&lt;!-- ------------------------ --&gt;\n","\u003Ch2\u003EData Transformations With SQL\u003C/h2\u003E\n","\u003Cp\u003EWe now have the necessary data in our Snowflake account. To get closer to the insights that we need &ndash;&nbsp;weather-related data for the city of Hamburg, Germany &ndash;&nbsp;we need to use SQL to apply transformations to the data. This will bring us closer to the insights we're after. Let's begin.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EStart by navigating to the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/01_transformation/hamburg_sales.sql\"\u003E\u003Cstrong\u003Ehamburg_sales.sql\u003C/strong\u003E\u003C/a\u003E file in the \u003Cstrong\u003E01_transformation\u003C/strong\u003E folder in repo. Copy the contents.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen a new SQL worksheet in Snowflake and paste the contents into the sheet.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EIn this worksheet, we use SQL to explore the suspicion that there are weather-related anomalies affecting sales in February in Hamburg, Germany. Let's start exploring.\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\n","\u003Cp\u003ERun the first few lines to set your context.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERun the first block of SQL that explores sales in Hamburg. You should encounter an error. We need to specify the city and country we're interested in exploring. On line 16, add \u003Ccode\u003E'Germany'\u003C/code\u003E as the country, and \u003Ccode\u003E'Hamburg'\u003C/code\u003E as the city. Re-run the block of SQL.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Query to explore sales in the city of Hamburg, Germany\nWITH _feb_date_dim AS (\n    SELECT DATEADD(DAY, SEQ4(), '2022-02-01') AS date \n    FROM TABLE(GENERATOR(ROWCOUNT =&gt; 28))\n)\nSELECT\n    fdd.date,\n    ZEROIFNULL(SUM(o.price)) AS daily_sales\nFROM _feb_date_dim fdd\nLEFT JOIN analytics.orders_v o\n    ON fdd.date = DATE(o.order_ts)\n    AND o.country = '#' -- Add country\n    AND o.primary_city = '#' -- Add city\nWHERE fdd.date BETWEEN '2022-02-01' AND '2022-02-28'\nGROUP BY fdd.date\nORDER BY fdd.date ASC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIt looks like the analysts were correct &ndash; there were several days in February where sales totaled 0.00. You can use the Chart feature to confirm this visually. We're on the right path!\u003C/strong\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ELet's now create a view that adds weather data &ndash; the data from our live data share we pulled from Snowflake Marketplace &ndash;&nbsp;to all cities where Tasty Bytes operates. Run the next block of SQL that creates the \u003Ccode\u003Etasty_bytes.harmonized.daily_weather_v\u003C/code\u003E view. We'll use this view later on in our pipeline.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create view that adds weather data for cities where Tasty Bytes operates\nCREATE OR REPLACE VIEW tasty_bytes.harmonized.daily_weather_v\nCOMMENT = 'Weather Source Daily History filtered to Tasty Bytes supported Cities'\n    AS\nSELECT\n    hd.*,\n    TO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,\n    pc.city_name AS city,\n    c.country AS country_desc\nFROM Pelmorex_Weather_Source_frostbyte.onpoint_id.history_day hd\nJOIN Pelmorex_Weather_Source_frostbyte.onpoint_id.postal_codes pc\n    ON pc.postal_code = hd.postal_code\n    AND pc.country = hd.country\nJOIN TASTY_BYTES.raw_pos.country c\n    ON c.iso_country = hd.country\n    AND c.city = hd.city_name;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EThe next block of SQL helps us query this view to explore temperatures for the month of February in Hamburg. Run the block of code. Click on &quot;Chart&quot; once the block of code executes. Nothing seems to be out of the ordinary here...\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Query the view to explore daily temperatures in Hamburg, Germany for anomalies\nSELECT\n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    AVG(dw.avg_temperature_air_2m_f) AS avg_temperature_air_2m_f\nFROM harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc = 'Germany'\n    AND dw.city_name = 'Hamburg'\n    AND YEAR(date_valid_std) = '2022'\n    AND MONTH(date_valid_std) = '2' -- February\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003ERun the next query that explores wind speeds in Hamburg. Once again, click &quot;Chart&quot; to visualize this data. Wow &ndash; there were spikes in windspeed that approached hurricane-force winds. \u003Cstrong\u003EIt looks like this windspeed affected sales during the month of February!\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Query the view to explore wind speeds in Hamburg, Germany for anomalies\nSELECT\n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM tasty_bytes.harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc IN ('Germany')\n    AND dw.city_name = 'Hamburg'\n    AND YEAR(date_valid_std) = '2022'\n    AND MONTH(date_valid_std) = '2' -- February\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"8\"\u003E\u003Cli\u003ENow that we've found a likely culprit behind the drop in sales, so let's create a view that tracks windspeed. We'll use this view later on in our pipeline. Locate the next block of SQL. Add \u003Ccode\u003Ewindspeed_hamburg\u003C/code\u003E to the end of the first line, so that the line reads \u003Ccode\u003ECREATE OR REPLACE VIEW tasty_bytes.harmonized.windspeed_hamburg\u003C/code\u003E and names our view. Run the final block of SQL to create the view.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create a view that tracks windspeed for Hamburg, Germany\nCREATE OR REPLACE VIEW tasty_bytes.harmonized. --add name of view\n    AS\nSELECT\n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc IN ('Germany')\n    AND dw.city_name = 'Hamburg'\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA view in Snowflake allows you to store and access the \u003Cstrong\u003Eresult\u003C/strong\u003E of a query. This is in contrast to querying multiple tables with raw data to extract insights. The queries that views store can be arbitrarily simple or complex. This helps you query exactly what you need at a much faster speed.\u003C/p\u003E\n","\u003Cp\u003EViews also help organize exactly which aspects of data might be valuable, and help with secure data access control. We can use views to power our data pipeline, without sacrificing performance.\u003C/p\u003E\n","\u003Cp\u003EWe'll use these views in our pipeline later on.\u003C/p\u003E\n&lt;!-- ------------------------ --&gt;\n","\u003Ch2\u003ECreate User-Defined Functions For Calculations\u003C/h2\u003E\n","\u003Cp\u003EWe're missing some critical data for our pipeline. Our analysts have requested that we track certain weather measurements using the metric system. We are tracking a country in Europe after all.\u003C/p\u003E\n","\u003Cp\u003EThey've specifically requested that we include temperature measurements in Celsius, and precipitation in millimeters.\u003C/p\u003E\n","\u003Cp\u003ETo do this, we'll create two user-defined functions, or UDFs, that are able to take in existing data from our tables and perform the necessary conversions. We will then invoke them later on to derive these new values.\u003C/p\u003E\n","\u003Cp\u003ELet's get started.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy the contents of the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/01_transformation/udf.sql\"\u003E\u003Cstrong\u003Eudf.sql\u003C/strong\u003E\u003C/a\u003E file in the \u003Cstrong\u003E01_transformation\u003C/strong\u003E folder in the repo.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen a new SQL worksheet in Snowflake and paste the contents in.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThese two blocks of SQL each create a UDF. One converts temperatures from Fahrenheit to Celsius, and the other converts inches to millimeters.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWe need to finish these SQL statements before we can run this code and create the functions. Add the word \u003Ccode\u003EFUNCTION\u003C/code\u003E after each the \u003Ccode\u003ECREATE OR REPLACE\u003C/code\u003E statements. Be sure to remove the \u003Ccode\u003E/* */\u003C/code\u003E placeholders.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE /*  */ tasty_bytes.analytics.fahrenheit_to_celsius(temp_f NUMBER(35,4))\n  RETURNS NUMBER(35,4)\n  AS\n  $$\n    (temp_f - 32) * (5/9)\n  $$\n;\n\nCREATE OR REPLACE /*  */ tasty_bytes.analytics.inch_to_millimeter(inch NUMBER(35,4))\n  RETURNS NUMBER(35,4)\n  AS\n  $$\n    inch * 25.4\n  $$\n;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003EAfter completing the SQL statements, run the entire worksheet. You should see a success message, and you can verify the creation of the functions in the \u003Cstrong\u003Etasty_bytes.analytics\u003C/strong\u003E schema.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EGreat! We'll use these functions to expand the views we're planning on using in our pipeline.\u003C/p\u003E\n&lt;!-- ------------------------ --&gt;\n","\u003Ch2\u003EApply UDFs For Data Transformations\u003C/h2\u003E\n","\u003Cp\u003ELet's now use the UDFs to add new columns in our views. These new columns will contains the converted values for temperature and precipitation.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy the contents of the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/01_transformation/updated_hamburg_sales.sql\"\u003E\u003Cstrong\u003Eupdated_hamburg_sales.sql\u003C/strong\u003E\u003C/a\u003E file in the \u003Cstrong\u003E01_transformation\u003C/strong\u003E folder in repo.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen a new SQL worksheet in Snowflake and paste the contents in.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENotice that on lines 14 and 16, we call the UDFs and apply them against existing values, and then use the output of those functions to create new columns with corresponding data.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Apply UDFs and confirm successful execution\n...\n-- Code to focus on\n    ROUND(AVG(analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\n    ROUND(AVG(analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\n\n...\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\n","\u003Cp\u003EName the view. On line 6, finish the statement by adding \u003Ccode\u003Eweather_hamburg\u003C/code\u003E. The entire line should read \u003Ccode\u003ECREATE OR REPLACE VIEW harmonized.weather_hamburg\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERun the entire file top to bottom. You should see a successful message, and you can confirm the creation of the view using the object picker on the left.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EGreat job! We've transformed our data using SQL to create views, and we've used UDFs to expand our views (and create new views!). We'll use these transformations later on in our pipeline.\u003C/p\u003E\n","\u003Cp\u003EThis completes the \u003Cstrong\u003ETransformation\u003C/strong\u003E aspect of our pipeline for this lab.\u003C/p\u003E\n&lt;!-- ------------------------ --&gt;\n","\u003Ch2\u003EDeliver Insights With Streamlit in Snowflake\u003C/h2\u003E\n","\u003Cp\u003EWe now have the insights that we need, and we can now also deliver them to our data analysts. We specifically have views that track the weather and sales in Hamburg, Germany. So how exactly will we make these insights easily accessible for our data analysts?\u003C/p\u003E\n","\u003Cp\u003EWe're going to create a Streamlit in Snowflake app that will visualize this data for them.\u003C/p\u003E\n","\u003Cp\u003EFor some context, Streamlit is a popular open-source Python library for creating data apps in Python &ndash;&nbsp;no need for HTML, CSS, or any other front-end frameworks. It's also natively available within Snowflake, which means creating data apps that use the data in our Snowflake environment is both powerful and easy to do.\u003C/p\u003E\n","\u003Cp\u003ELet's go ahead and create the app for our analysts.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy the contents of the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/02_delivery/streamlit.py\"\u003E\u003Cstrong\u003Estreamlit.py\u003C/strong\u003E\u003C/a\u003E file in the \u003Cstrong\u003E02_delivery\u003C/strong\u003E folder in the repo.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to &quot;Projects&quot;, then click on &quot;Streamlit&quot;.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAt the top, create a new Streamlit app. Name the app &quot;HAMBURG_GERMANY_TRENDS&quot;.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFor the app location, select \u003Cstrong\u003ETASTY_BYTES\u003C/strong\u003E as the database, and \u003Cstrong\u003EHARMONIZED\u003C/strong\u003E for the schema. Leave everything else as-is.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate the app and wait for it to boot up. You'll notice a default, sample app rendered for you automatically.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIf the code editor is not present, click on &quot;Edit&quot; at the top. Otherwise, you can see the code powering this sample app in the Python editor in Snowflake.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EDelete all of this sample code.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste in the Python code you copied earlier from the \u003Cstrong\u003Estreamlit.py\u003C/strong\u003E file.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis code creates an app that visualizes the data within the views we created earlier.\u003C/p\u003E\n\u003Col start=\"9\"\u003E\u003Cli\u003E\n","\u003Cp\u003EClick &quot;Run&quot; at the top. You should encounter an error. On line 14, replace \u003Ccode\u003E&quot;INSERT NAME OF VIEW HERE&quot;\u003C/code\u003E with \u003Ccode\u003Etasty_bytes.harmonized.weather_hamburg\u003C/code\u003E and click &quot;Run once more.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe application should render successfully! Congratulations!\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-northstar-data-engineering/app.png?v=69367252\" alt=\"app\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn just 54 lines of code, we created a Streamlit in Snowflake application with Python that delivers the insights we extracted from our data.\u003C/p\u003E\n","\u003Cp\u003EAt the top, you could imagine clicking on &quot;Share&quot; and sharing the app with relevant analyst roles in your Snowflake account.\u003C/p\u003E\n","\u003Cp\u003EWith this application, we've now completed our end-to-end data pipeline. This completes the \u003Cstrong\u003EDelivery\u003C/strong\u003E aspect of our pipeline for this lab.\u003C/p\u003E\n&lt;!-- ------------------------ --&gt;\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003ECongratulations! You've built an end-to-end data pipeline in Snowflake using the \u003Cstrong\u003EIngestion-Transformation&ndash;Delivery\u003C/strong\u003E framework, also known as \u003Cstrong\u003EI-T-D\u003C/strong\u003E. Let's recap what you did.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EYou built a data pipeline that tracks weather and sales data for Tasty Bytes food trucks in the city of Hamburg, Germany. As part of the I-T-D framework, you:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIngestion\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ELoaded data from:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake Marketplace\u003C/li\u003E\u003Cli\u003EAWS S3\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETransformation\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ETransformed data using:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESQL\u003C/li\u003E\u003Cli\u003EViews\u003C/li\u003E\u003Cli\u003EUser-defined functions (UDFs)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDelivery\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EDelivered a final data product using:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EStreamlit in Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECongratulations!\u003C/p\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n","\u003Cp\u003EFor more resources, check out the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EYou can build the objects we used in the pipeline using Snowpark for Python as well. The code to do this is provided in the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/01_transformation/hamburg_sales_snowpark.ipynb\"\u003E\u003Cstrong\u003Ehamburg_sales_snowpark.ipynb\u003C/strong\u003E Notebook file in the repo\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELearn more at \u003Ca href=\"/en/developers/northstar/\"\u003ESnowflake Northstar\u003C/a\u003E for developers.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Getting Started – Data Engineering with Snowflake",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\n### Overview\n\nIn this Quickstart, we're going to focus on data engineering with Snowflake. We'll specifically build an end-to-end data pipeline with Snowflake. We'll apply the  **Ingestion-Transformation–Delivery** framework, also known as **I-T-D**, to build the pipeline.\n\n**Ingestion**\n\nWe'll load data from:\n\n* Snowflake Marketplace\n* AWS S3\n\n**Transformation**\n\nWe'll transform our data using:\n\n* SQL\n* Views\n* User-defined functions (UDFs)\n\n**Delivery**\n\nWe'll deliver a final data product using:\n\n* Streamlit in Snowflake\n\n\n### Prerequisites\n- Some basic familiarity with SQL\n\n### What You’ll Learn \n- Snowflake for data engineering\n- The Ingestion-Transformation-Delivery framework, or I-T-D, for data pipelines\n- Data sharing from Snowflake Marketplace\n- How to load data from AWS S3 blob storage\n- How to perform transformations against data using SQL, Python, Views, UDFs, Stored Procedures\n- How to deliver an end data product using Streamlit in Snowflake\n\n### What You’ll Need \n- A free Snowflake trial account: [https://signup.snowflake.com/](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&trial=student&cloud=aws&region=us-west-2&utm_campaign=introtosnowflake&utm_cta=developer-guides)\n- The companion GitHub repo that contains the code to complete this lab: [sfguide-snowflake-northstar-data-engineering](https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering)\n\n### What You’ll Build \n- An end-to-end data pipeline in Snowflake\n\n\u003C!-- ------------------------ --\u003E\n## Open a Snowflake Trial Account\n\nTo complete this lab, you'll need a Snowflake account. A free Snowflake trial account will work just fine. To open one:\n\n1. Navigate to [https://signup.snowflake.com/](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&trial=student&cloud=aws&region=us-west-2&utm_campaign=introtosnowflake&utm_cta=developer-guides)\n\n2. Start the account creation by completing the first page of the form on the page.\n\n3. On the next section of the form,  be sure to set the Snowflake edition to \"Enterprise (Most popular\").\n\n4. Select \"AWS – Amazon Web Services\" as the cloud provider.\n\n5. Select \"US West (Oregon)\" as the region.\n\n6. Complete the rest of the form and click \"Get started\".\n\n![trial](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-northstar-data-engineering/trial.png?v=69367252)\n\n\u003C!-- ------------------------ --\u003E\n## Understand The Pipeline We'll Build\n\nTasty Bytes is a food truck company that operates globally in many countries. You're a data engineer on the Tasty Bytes team, and you've recently learned from data analysts on the team that:\n\n* Sales in the city of Hamburg, Germany dropped to $0 for a few days in the month of February\n\nAs a data engineer, your goal is to figure out why this happened, and to also build and end-to-end data pipeline that can  keep analysts up-to-date on the weather in Hamburg.\n\nHere's how we'll do this:\n\n**Ingestion**\n\n* Load live weather data from Snowflake Marketplace\n\n* Load Tasty Bytes sales data from an AWS S3 bucket\n\n**Transformation**\n\n* Use SQL to perform transformations\n\n* Create views that we can use for data analytics\n\n* Create user-defined functions (UDFs) that perform weather-related calculations to enrich the views with more data\n\n**Deliver**\n\n* Deliver final insights and a live graph of weather in Hamburg, Germany using Streamlit in Snowflake (write the application in Python)\n\nLet's get started!\n\n\u003C!-- ------------------------ --\u003E\n## Weather Data From Snowflake Marketplace\n\nLet's start by \"loading\" the raw weather data into Snowflake. It turns out that \"loading\" is really the wrong word here. \n\nWe're using Snowflake's unique data sharing capability in Snowflake Marketplace. Because of this, we don't actually need to copy any data to our Snowflake account with any logic. Instead, we can directly access the weather data shared by a trusted provide in Snowflake Marketplace. Let's go ahead and do this.\n\n1. Log into your Snowflake account.\n\n2. Click on \"Marketplace\".\n\n3. Click on \"Snowflake Marketplace\". \n\n4. In the search bar, search for \"pelmorex frostbyte\".\n\n5. The first result should be \"Pelmorex Weather Source: Frostbyte\" by the \"Pelmorex Weather Source\" provider. Click on the listing.\n\n6. On the right, click \"Get\".\n\n7. In the ensuing modal, click \"Get\" once more. Do **NOT** rename the dataset.\n\nThis is a live dataset! No need to write ingestion logic to bring the data into your account. The data is maintained and kept fresh by the provider.\n\n![data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-northstar-data-engineering/weathersource.png?v=69367252)\n\n\u003C!-- ------------------------ --\u003E\n## Load Sales Data From AWS S3\n\nLet's now load the Tasty Bytes sales data. This data is currently sitting across many CSV files in an AWS S3 bucket. Let's use Snowflake's COPY INTO command to load the data into your Snowflake account.\n\n1. Start by creating a new SQL worksheet.\n\n2. Set your context. Set your role to **ACCOUNTADMIN**, and set your compute resource (virtual warehouse) to **COMPUTE_WH**.\n\n3. Next, create the database and schema to store the data that we'll load. Type the following into the worksheet:\n\n```sql\nCREATE OR REPLACE DATABASE tasty_bytes;\nCREATE OR REPLACE SCHEMA raw_pos;\n```\n\n4. Run this code by highlighting these two statements and clicking the Run button at the top right of the SQL worksheet.\n\n5. Next, navigate to the [**copy_into.sql**](https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/00_ingestion/copy_into.sql) file in the **00_ingestion** folder of the **sfguide-snowflake-northstar-data-engineering** repo. Copy the contents of this file.\n\n6. Navigate back to the SQL worksheet you opened. Paste in the code you copied under the two lines of SQL you wrote in the previous step.\n\n7. Start by creating the file format necessary to load the data into Snowflake. Type the following underneath the \"Create a CSV file format here:\" comment:\n\n```sql\nCREATE OR REPLACE FILE FORMAT tasty_bytes.public.csv_ff\ntype = 'csv';\n```\n\nRun this command.\n\n8. In the next block of SQL, with the comment `-- Specify the file format below:`, we create the Snowflake stage that points to the S3 bucket containing the CSV files. It's currently missing the file format argument that's necessary during the loading process. Below the `url` parameter, type:\n\n```sql\nfile_format = tasty_bytes.public.csv_ff;\n```\n\nRun this entire command.\n\n9. The next block of SQL creates a table called **COUNTRY** in the **tasty_bytes.raw_pos** schema. Run this block of SQL to create the table. Confirm the creation of the table using the object picker on the left-hand side.\n\n10. Let's bring it all together now! After creating the table, let's now load the data into this table using the powerful **COPY INTO** command. At the very bottom of the worksheet, underneath the \"Use the COPY INTO command...\" comment, type:\n\n```sql\nCOPY INTO tasty_bytes.raw_pos.country\nFROM @tasty_bytes.public.s3load/raw_pos/country/;\n```\n\nRun this command. You should see a success message in the console. Great job!\n\n**We're not quite done.** We loaded about 30 rows of data into the **COUNTRY** table, but we actually need to load close to 1 GB of sales data. We won't do this line-by-line. Instead, you'll use some SQL that we've written for you in advance.\n\n1. Navigate to the [**load_tasty_bytes.sql**](https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/00_ingestion/load_tasty_bytes.sql) file in the **00_ingestion** folder within the **sfguide-snowflake-northstar-data-engineering** companion repo. Copy the contents. \n\n2. Navigate back to Snowflake and create a new SQL worksheet. Paste the contents you copied into the worksheet.\n\n3. Run the entire file at once by clicking the drop-down at the top right, and clicking \"Run All\".\n\nObservations:\n\n* This will load close to 1 GB of Tasty Bytes sales data from AWS S3 into your Snowflake account.\n\n* The script programmatically creates an XL compute resource (virtual warehouse) used for the loading of the data. Once the loading is done, the compute resource is dropped.\n\n* This worksheet contains about 300 lines of SQL. The powerful thing is that you can understand 99% of it because of the SQL you wrote earlier to load data into the **COUNTRY** table.\n\nAfter running the file, you should have all of the data loaded into your account! Confirm the creation of all the tables (and their data) using the object picker to the left.\n\nThis completes the **Ingestion** aspect of our pipeline for this lab.\n\n\u003C!-- ------------------------ --\u003E\n## Data Transformations With SQL\n\nWe now have the necessary data in our Snowflake account. To get closer to the insights that we need – weather-related data for the city of Hamburg, Germany – we need to use SQL to apply transformations to the data. This will bring us closer to the insights we're after. Let's begin.\n\n1. Start by navigating to the [**hamburg_sales.sql**](https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/01_transformation/hamburg_sales.sql) file in the **01_transformation** folder in repo. Copy the contents.\n\n2. Open a new SQL worksheet in Snowflake and paste the contents into the sheet.\n\nIn this worksheet, we use SQL to explore the suspicion that there are weather-related anomalies affecting sales in February in Hamburg, Germany. Let's start exploring.\n\n3. Run the first few lines to set your context.\n\n4. Run the first block of SQL that explores sales in Hamburg. You should encounter an error. We need to specify the city and country we're interested in exploring. On line 16, add `'Germany'` as the country, and `'Hamburg'` as the city. Re-run the block of SQL.\n\n```sql\n-- Query to explore sales in the city of Hamburg, Germany\nWITH _feb_date_dim AS (\n    SELECT DATEADD(DAY, SEQ4(), '2022-02-01') AS date \n    FROM TABLE(GENERATOR(ROWCOUNT =\u003E 28))\n)\nSELECT\n    fdd.date,\n    ZEROIFNULL(SUM(o.price)) AS daily_sales\nFROM _feb_date_dim fdd\nLEFT JOIN analytics.orders_v o\n    ON fdd.date = DATE(o.order_ts)\n    AND o.country = '#' -- Add country\n    AND o.primary_city = '#' -- Add city\nWHERE fdd.date BETWEEN '2022-02-01' AND '2022-02-28'\nGROUP BY fdd.date\nORDER BY fdd.date ASC;\n```\n\n**It looks like the analysts were correct – there were several days in February where sales totaled 0.00. You can use the Chart feature to confirm this visually. We're on the right path!**\n\n5. Let's now create a view that adds weather data – the data from our live data share we pulled from Snowflake Marketplace – to all cities where Tasty Bytes operates. Run the next block of SQL that creates the `tasty_bytes.harmonized.daily_weather_v` view. We'll use this view later on in our pipeline.\n\n```sql\n-- Create view that adds weather data for cities where Tasty Bytes operates\nCREATE OR REPLACE VIEW tasty_bytes.harmonized.daily_weather_v\nCOMMENT = 'Weather Source Daily History filtered to Tasty Bytes supported Cities'\n    AS\nSELECT\n    hd.*,\n    TO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,\n    pc.city_name AS city,\n    c.country AS country_desc\nFROM Pelmorex_Weather_Source_frostbyte.onpoint_id.history_day hd\nJOIN Pelmorex_Weather_Source_frostbyte.onpoint_id.postal_codes pc\n    ON pc.postal_code = hd.postal_code\n    AND pc.country = hd.country\nJOIN TASTY_BYTES.raw_pos.country c\n    ON c.iso_country = hd.country\n    AND c.city = hd.city_name;\n```\n\n6. The next block of SQL helps us query this view to explore temperatures for the month of February in Hamburg. Run the block of code. Click on \"Chart\" once the block of code executes. Nothing seems to be out of the ordinary here...\n\n```sql\n-- Query the view to explore daily temperatures in Hamburg, Germany for anomalies\nSELECT\n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    AVG(dw.avg_temperature_air_2m_f) AS avg_temperature_air_2m_f\nFROM harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc = 'Germany'\n    AND dw.city_name = 'Hamburg'\n    AND YEAR(date_valid_std) = '2022'\n    AND MONTH(date_valid_std) = '2' -- February\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\n```\n\n7. Run the next query that explores wind speeds in Hamburg. Once again, click \"Chart\" to visualize this data. Wow – there were spikes in windspeed that approached hurricane-force winds. **It looks like this windspeed affected sales during the month of February!**\n\n```sql\n-- Query the view to explore wind speeds in Hamburg, Germany for anomalies\nSELECT\n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM tasty_bytes.harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc IN ('Germany')\n    AND dw.city_name = 'Hamburg'\n    AND YEAR(date_valid_std) = '2022'\n    AND MONTH(date_valid_std) = '2' -- February\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\n```\n\n8. Now that we've found a likely culprit behind the drop in sales, so let's create a view that tracks windspeed. We'll use this view later on in our pipeline. Locate the next block of SQL. Add `windspeed_hamburg` to the end of the first line, so that the line reads `CREATE OR REPLACE VIEW tasty_bytes.harmonized.windspeed_hamburg` and names our view. Run the final block of SQL to create the view.\n\n```sql\n-- Create a view that tracks windspeed for Hamburg, Germany\nCREATE OR REPLACE VIEW tasty_bytes.harmonized. --add name of view\n    AS\nSELECT\n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc IN ('Germany')\n    AND dw.city_name = 'Hamburg'\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\n```\n\nA view in Snowflake allows you to store and access the **result** of a query. This is in contrast to querying multiple tables with raw data to extract insights. The queries that views store can be arbitrarily simple or complex. This helps you query exactly what you need at a much faster speed. \n\nViews also help organize exactly which aspects of data might be valuable, and help with secure data access control. We can use views to power our data pipeline, without sacrificing performance.\n\nWe'll use these views in our pipeline later on.\n\n\u003C!-- ------------------------ --\u003E\n## Create User-Defined Functions For Calculations\n\nWe're missing some critical data for our pipeline. Our analysts have requested that we track certain weather measurements using the metric system. We are tracking a country in Europe after all.\n\nThey've specifically requested that we include temperature measurements in Celsius, and precipitation in millimeters.\n\nTo do this, we'll create two user-defined functions, or UDFs, that are able to take in existing data from our tables and perform the necessary conversions. We will then invoke them later on to derive these new values.\n\nLet's get started.\n\n1. Copy the contents of the [**udf.sql**](https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/01_transformation/udf.sql) file in the **01_transformation** folder in the repo.\n\n2. Open a new SQL worksheet in Snowflake and paste the contents in.\n\n3. These two blocks of SQL each create a UDF. One converts temperatures from Fahrenheit to Celsius, and the other converts inches to millimeters. \n\n4. We need to finish these SQL statements before we can run this code and create the functions. Add the word `FUNCTION` after each the `CREATE OR REPLACE` statements. Be sure to remove the `/*  */` placeholders.\n\n```sql\nCREATE OR REPLACE /*  */ tasty_bytes.analytics.fahrenheit_to_celsius(temp_f NUMBER(35,4))\n  RETURNS NUMBER(35,4)\n  AS\n  $$\n    (temp_f - 32) * (5/9)\n  $$\n;\n\nCREATE OR REPLACE /*  */ tasty_bytes.analytics.inch_to_millimeter(inch NUMBER(35,4))\n  RETURNS NUMBER(35,4)\n  AS\n  $$\n    inch * 25.4\n  $$\n;\n```\n\n5. After completing the SQL statements, run the entire worksheet. You should see a success message, and you can verify the creation of the functions in the **tasty_bytes.analytics** schema.\n\nGreat! We'll use these functions to expand the views we're planning on using in our pipeline.\n\n\u003C!-- ------------------------ --\u003E\n## Apply UDFs For Data Transformations\n\nLet's now use the UDFs to add new columns in our views. These new columns will contains the converted values for temperature and precipitation.\n\n1. Copy the contents of the [**updated_hamburg_sales.sql**](https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/01_transformation/updated_hamburg_sales.sql) file in the **01_transformation** folder in repo.\n\n2. Open a new SQL worksheet in Snowflake and paste the contents in.\n\n3. Notice that on lines 14 and 16, we call the UDFs and apply them against existing values, and then use the output of those functions to create new columns with corresponding data.\n\n```sql\n-- Apply UDFs and confirm successful execution\n...\n-- Code to focus on\n    ROUND(AVG(analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\n    ROUND(AVG(analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\n\n...\n```\n\n4. Name the view. On line 6, finish the statement by adding `weather_hamburg`. The entire line should read `CREATE OR REPLACE VIEW harmonized.weather_hamburg`.\n\n5. Run the entire file top to bottom. You should see a successful message, and you can confirm the creation of the view using the object picker on the left.\n\nGreat job! We've transformed our data using SQL to create views, and we've used UDFs to expand our views (and create new views!). We'll use these transformations later on in our pipeline.\n\nThis completes the **Transformation** aspect of our pipeline for this lab.\n\n\u003C!-- ------------------------ --\u003E\n## Deliver Insights With Streamlit in Snowflake\n\nWe now have the insights that we need, and we can now also deliver them to our data analysts. We specifically have views that track the weather and sales in Hamburg, Germany. So how exactly will we make these insights easily accessible for our data analysts?\n\nWe're going to create a Streamlit in Snowflake app that will visualize this data for them.\n\nFor some context, Streamlit is a popular open-source Python library for creating data apps in Python – no need for HTML, CSS, or any other front-end frameworks. It's also natively available within Snowflake, which means creating data apps that use the data in our Snowflake environment is both powerful and easy to do.\n\nLet's go ahead and create the app for our analysts.\n\n1. Copy the contents of the [**streamlit.py**](https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/02_delivery/streamlit.py) file in the **02_delivery** folder in the repo.\n\n2. Navigate to \"Projects\", then click on \"Streamlit\".\n\n3. At the top, create a new Streamlit app. Name the app \"HAMBURG_GERMANY_TRENDS\".\n\n4. For the app location, select **TASTY_BYTES** as the database, and **HARMONIZED** for the schema. Leave everything else as-is.\n\n5. Create the app and wait for it to boot up. You'll notice a default, sample app rendered for you automatically.\n\n6. If the code editor is not present, click on \"Edit\" at the top. Otherwise, you can see the code powering this sample app in the Python editor in Snowflake.\n\n7. Delete all of this sample code.\n\n8. Paste in the Python code you copied earlier from the **streamlit.py** file.\n\nThis code creates an app that visualizes the data within the views we created earlier. \n\n9. Click \"Run\" at the top. You should encounter an error. On line 14, replace `\"INSERT NAME OF VIEW HERE\"` with `tasty_bytes.harmonized.weather_hamburg` and click \"Run once more.\n\n10. The application should render successfully! Congratulations!\n\n![app](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-northstar-data-engineering/app.png?v=69367252)\n\nIn just 54 lines of code, we created a Streamlit in Snowflake application with Python that delivers the insights we extracted from our data. \n\nAt the top, you could imagine clicking on \"Share\" and sharing the app with relevant analyst roles in your Snowflake account.\n\nWith this application, we've now completed our end-to-end data pipeline. This completes the **Delivery** aspect of our pipeline for this lab.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\n### Conclusion\n\nCongratulations! You've built an end-to-end data pipeline in Snowflake using the **Ingestion-Transformation–Delivery** framework, also known as **I-T-D**. Let's recap what you did. \n\n### What You Learned\n\nYou built a data pipeline that tracks weather and sales data for Tasty Bytes food trucks in the city of Hamburg, Germany. As part of the I-T-D framework, you:\n\n**Ingestion**\n\nLoaded data from:\n\n* Snowflake Marketplace\n* AWS S3\n\n**Transformation**\n\nTransformed data using:\n\n* SQL\n* Views\n* User-defined functions (UDFs)\n\n**Delivery**\n\nDelivered a final data product using:\n\n* Streamlit in Snowflake\n\nCongratulations! \n\n### Resources\n\nFor more resources, check out the following:\n\n* You can build the objects we used in the pipeline using Snowpark for Python as well. The code to do this is provided in the [**hamburg_sales_snowpark.ipynb** Notebook file in the repo](https://github.com/Snowflake-Labs/sfguide-snowflake-northstar-data-engineering/blob/main/01_transformation/hamburg_sales_snowpark.ipynb).\n\n* Learn more at [Snowflake Northstar](/en/developers/northstar/) for developers.\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-677f01fa67","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-98e845babc",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-47c1c439ec","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-24",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-b481985d21","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-122ee9f7bf",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-53113e479d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-5f4210b85b","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-37423e2a0a",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/snowflake-northstar-data-engineering","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EOpen a Snowflake Trial Account\u003C/h2\u003E","\u003Ch2\u003EUnderstand The Pipeline We'll Build\u003C/h2\u003E","\u003Ch2\u003EWeather Data From Snowflake Marketplace\u003C/h2\u003E","\u003Ch2\u003ELoad Sales Data From AWS S3\u003C/h2\u003E","\u003Ch2\u003EData Transformations With SQL\u003C/h2\u003E","\u003Ch2\u003ECreate User-Defined Functions For Calculations\u003C/h2\u003E","\u003Ch2\u003EApply UDFs For Data Transformations\u003C/h2\u003E","\u003Ch2\u003EDeliver Insights With Streamlit in Snowflake\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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